Extreme Differences in Forest Degradation in Borneo: Comparing Practices in Sarawak, Sabah, and Brunei

<div><p>The Malaysian states of Sabah and Sarawak are global hotspots of forest loss and degradation due to timber and oil palm industries; however, the rates and patterns of change have remained poorly measured by conventional field or satellite approaches. Using 30 m resolution optical imagery acquired since 1990, forest cover and logging roads were mapped throughout Malaysian Borneo and Brunei using the Carnegie Landsat Analysis System. We uncovered ∼364,000 km of roads constructed through the forests of this region. We estimated that in 2009 there were at most 45,400 km² of intact forest ecosystems in Malaysian Borneo and Brunei. Critically, we found that nearly 80% of the land surface of Sabah and Sarawak was impacted by previously undocumented, high-impact logging or clearing operations from 1990 to 2009. This contrasted strongly with neighbouring Brunei, where 54% of the land area remained covered by unlogged forest. Overall, only 8% and 3% of land area in Sabah and Sarawak, respectively, was covered by intact forests under designated protected areas. Our assessment shows that very few forest ecosystems remain intact in Sabah or Sarawak, but that Brunei, by largely excluding industrial logging from its borders, has been comparatively successful in protecting its forests.</p></div

Forest cover and condition in Malaysian Borneo and Brunei in 2009.

<p>Intact forest, degraded forest, mangroves, plantations and regrowth are shown.</p

Land cover in 2009 in Sabah, Sarawak and Brunei mapped using CLASlite.

1<p>Not roaded;</p>2<p>Roaded once;</p>3<p>Roaded 2–7 times.</p

Thirteen airborne LiDAR study blocks were used to map depositional-floodplain (DFP) substrates (68, 958 ha) and a variety of erosional <i>terra firme</i> (ETF) substrates (56,623 ha).

<p>The <i>terra firme</i> zones are described in terms of basic geologic, topographic and physiognomic composition.</p

Canopy structure and gap statistics for CAO mapping block 12 including: (a) the distribution of canopy height for erosional <i>terra firme</i> (ETF) and depositional floodplain (DFP) forests (see Figure 2); (b) the vertical distribution of power-law exponents (λ) for each forest type in block 12; and (c) the gap-size frequency distributions for ETF and DFP forests for canopy gaps at <1 m and <20 m thresholds.

<p>Power-law exponents (λ) and the number of mapped gaps (n) are also provided.</p

Graphical representation of size-frequency distributions of canopy gaps on erosional <i>terra</i> firme and depositional floodplain substrates in the southwestern Peruvian Amazon at two height thresholds.

<p>The slopes of these lines are power-law exponents from the Zeta distribution that we estimated using maximum likelihood. Additional details are in Appendices S1 and S2.</p

One 13,883 ha Amazonian landscape (block 12;Figure 1) showing (a) the digital terrain model with additional processing to delineate depositional floodplain (DFP; red) and erosional <i>terra firme</i> (ETF; white) substrates; (b) forest canopy height derived from 3-D imaging; and zoom images to indicate differences in height and gap variation within (c) DFP and (d) ETF forests.

<p>Each zoom image is 50 ha in size. Individual crowns are visible in red colors; forest canopy gaps are indicated in blue.</p

Thirteen CAO LiDAR mapping blocks were acquired in the southern Peruvian Amazon.

<p>The upper inset shows location of the study region within Peru. The lower inset shows the LiDAR mapping blocks against a map of aboveground carbon density (ACD; Mg C ha⁻¹), which integrates regional variation in geology, topography and canopy physiognomy <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060875#pone.0060875-Asner4" target="_blank">[20]</a>.</p

Mean canopy height (± standard deviation) of forests on depositional-floodplain (DFP) and erosional <i>terra firme</i> (ETF) substrates (see Table 1), along with Zeta distribution (power-law) exponents (<b>λ</b>) of the gap-size frequency distributions for each site.

<p>Values are provided for gaps reaching to the ground level (<b>λ1</b> or ≥1 m) and for gaps found only in the upper canopy (<b>λ</b>20 or ≥20 m). Values in parentheses indicate the number of gaps mapped in each landscape.</p

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy

<div><p>Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods—binary support vector machine (SVM) and biased SVM—for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer’s accuracies of 94–97% for the three focal species, and field validation of the predicted crown objects indicated that these had user’s accuracies of 94–100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.</p></div